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Statistical efficiency in machine learning refers to how well a model utilizes the available data to make accurate predictions. A statistically efficient model extracts the maximum amount of information from the data, minimizing the number of observations needed to achieve a certain level of performance. Essentially, it's about getting the most bang for your buck in terms of data input.

Efficient models can provide accurate predictions with a smaller dataset, which is crucial in situations where data collection is expensive or time-consuming. Techniques like regularization, feature selection, and proper model tuning contribute to improving statistical efficiency by preventing overfitting and focusing on the most relevant information in the data.

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